Retail stores, and their suppliers, normally decide how to stock inventory according to several methods, such as comparing the rate of sales to the current perpetual inventory for an item, and forecasting, using any number of algorithms, when a stock out will occur. Reorder is then timed to the forecast stock-out such that a stock-out will not occur. This first method fails because either the perpetual inventory was incorrect—a frequent condition—the sales pattern assumed by the forecasting was incorrect; also a frequent condition.
Another methodology involves performing periodic examination of shelves to see what is not in stock or near out-of-stock and placing an order based on that condition. This second method fails because the person checking the shelves did not observe a stock-out condition, was not able to identify it (for example, because the “hole” was filled with another item—which often occurs in the retail industry), or did not report it, or it was reported but the proper action was not taken.
Stock-out conditions are generally considered undesirable by the retailer and by the retailer's suppliers. The retailer does not like it because it lowers customer service levels (i.e. their customers can't get what they want, and therefore may not shop there again), and because in many cases people will not buy a substitute product, and the potential sale will be lost. Supply chain partners do not like stock-outs because of lost sales, and because of the disruption in the normal flow of goods, which causes increased order and shipping costs.
Because of the problems with maintaining accurate perpetual inventory, stock-out conditions persist for up to 15% of all items in all stores on average. This constitutes at least a potential 15% efficiency gain opportunity across the retail goods trade. Moreover, improved stocking does not necessarily increase costs, so even a small improvement can yield a relatively large increase in profits.